For all agricultural sectors, achieving a high level of technical efficiency is essential for competitiveness and profitability. Cattle raising has been identified as a main products in Iran agriculture beef production and has not been sufficient for domestic consumption. Iran meat consumption per capita is about 12 kg and 65244 tons meat were imported in 2007. In the last two decades, several agricultural policies, particularly in Iran, have attempted to address questions about the economic efficiency of the Iranian native cattle based on the assumption that it is inefficient for production. To design and implement meaningful policies, there needs to be precise measures of efficiency that show what does and does not influence it.
These measures are necessary to understand the magnitude of the public policy challenge. There are causes for worry concerning the future development of cattle production in Iran. First, cattle are normally raised by unskilled person. Second, the Iran government has significantly influenced on agriculture through a variety of policies.
Because of these factors, economists and policy makers have raised the question of the economic efficiency of Caspian cattle feedlot in Iran, especially at farm level. This study has been the first application of Data Envelopment Analysis (DEA) in order to measure and explain economic efficiency and its components of Caspian cattle feedlot farms in Iran. This enables more detailed understanding of the nature of economic efficiency in these farms.
Technical efficiency is a measure of how well the individual transforms inputs
into a set of outputs based on a given set of technology and economic factors
(Aigner et al., 1977; Kumbhakar
and Lovell, 2000).
Two individuals using the same set of inputs and technology may produce considerably
different levels of output. While part of the difference may just be random
variations found in all aspects of life, other parts may be attributed to individual
fundamental attributes and to opportunities that could be influenced through
public policies (Ortega, 2002).
Likewise, efficiency has direct implications for the public welfare since resources
are used more effectively. DEA is a mathematical programming approach to assessing
relative efficiencies within a group of decision making units (DMUs) (Kao,
A decision unit can be each object, which can be characterized by inputs and
output. It is important that with an application of the DEA to a group of decision
units, all decision units have the same inputs and outputs. So, that the application
of the DEA supplies a meaningful result, only decision units should be considered
with an application, which are similar. The DEA makes possible for a user to
consider several inputs and several outputs. For the evaluation of the efficiency
of the decision units for each decision unit an efficiency value is computed.
With the application of the DEA to a group of decision units an optimization
problem must be solved for each decision unit. Many studies have been published
dealing with applying DEA in real-world situations. All efficiencies are restricted
to lie between zero and one (i.e., between 0 and 100%). In calculating the numerical
value for the efficiency of a particular DMU weights are chosen so as maximize
its efficiency, thereby presenting the DMU in the best possible light (Beasley,
The main purpose of this study is to measure and investigate factors affecting economic inefficiency and production improvement of cattle feedlot farms in Iran.
MATERIALS AND METHODS
The data used in this study is based on a direct interview survey of 70 farms that were selected by randomized sampling method classified with proportionate appointment in Guilan Province which are predominantly cattle producing areas in North of Iran (near Caspian Sea) during one fattening period. Questionnaires were completed and data such as, numbers of calve, farmers age, education and experience in years, monthly live weight, daily feed intake, metabolizable energy and crude protein intake of calve and length of fattening period were obtained. Also, cost of inputs and value of output were obtained.
This study applied the DEA approach to measure economic efficiency using the 2007 farm-level data of Caspian cattle feedlot farms. The farms selected were owner operated and had faced a similar economic and marketing environment for inputs and outputs.
One output and 6 inputs were used in the empirical application of this study. Six inputs were number of calve per farm, number of labour/days/hours, length of fattening period (days), total metabolizable energy intake (Mcal), total crude protein intake (kg) and total cost of hygiene-treatment of calve (Rials). The output were total live weight gain (kg) of calve per farm.
Charnes et al. (1978) proposed a model which
had an input orientation and assumed constant returns to scale (CRS). Subsequent
papers have considered alternative sets of assumptions, such as Banker
et al. (1984), who proposed a Variable Returns to Scale (VRS) model.
Following Fare et al. (1985), Banker
et al. (1996), Coelli et al. (2005)
and Sharma et al. (1999), the VRS model is discussed
below. Let us assume there is data available on K inputs and M outputs in each
of the N decision units (i.e., farms). Input and output vectors are represented
by the vectors xi and yi, respectively for the ith farm.
The data for all farms may be denoted by the KxN input matrix (X) and MxN output
The envelopment form of the input-oriented VRS DEA model is specified as:
where, θ is the input Technical Efficiency (TE) score having a value 0≤θ≤1.
If the θ value is one, indicating the farm is on the frontier, the vector
λ is an Nx1 vector of weights which defines the linear combination of the
peers of the ith farm. Thus, the linear programming problem needs to be solved
N times and a value of θ is provided for each farm in the sample.
The CRS DEA model is specified as:
In order to investigate the economic efficiency or cost efficiency, the cost
minimisation DEA is specified as:
where,wi is a vector of input prices for the I-th farm and xi*
is the cost-minimising vector of input quantities for the I-th farm. The economic
efficiency can be calculated as:
Allocative efficiency can be specified and calculated as:
Note that this procedure will include any slacks into the allocative efficiency
measure, reflecting an inappropriate input mix (Ferrier
and Lovell 1990). Efficiency scores in this study were estimated using the
computer program, DEAP Version 2.1 described by Coelli (1996).
The results showed in Table 1 and 2 that
for the case of Constant Returns to Scale (CRS), Mean technical, allocative
and cost efficiencies were 67.66, 80.57 and 53.5%, respectively and for the
case of Variable Returns to Scale (VRS), mean technical, allocative and cost
efficiencies were 87.23, 74.87 respectively.
||Inputs observed and recommended in CRS and VRS assumption
|n = 70
||Effect of farm size on crsTE
|a, b are statistically different groups
||Effect of farm size on vrsTE
||Effect of farmers age on crsTE
||Effect of farmers age on vrsTE
Also, for the case of CRS, minimum technical, allocative and cost efficiencies
were 23, 27 and 13%, respectively and for the case of VRS, mean technical, allocative
and cost efficiencies were 55, 25 and 23%, respectively. Efficiency summary
in CRS and VRS assumption in 70 fattening farms showed in Table
crsTE were significantly affected by farm size (p<0.05) (Table
3). vrsTE were not significantly affected by farm size (p>0.05) (Table
4). crsTE were not significantly affected by farmers age (p>0.05)
(Table 5). vrsTE were not significantly affected by farmers
age (p>0.05) (Table 6). crsTE were not significantly affected
by farmers education (p>0.05) (Table 7). vrsTE were
not significantly affected by farmers education (p>0.05) (Table
8). crsTE were not significantly affected by farmers experience (p>0.05)
(Table 9). vrsTE were not significantly affected by farmers
education (p>0.05), (Table 10).
||Effect of farmers education in years on crsTE
||Effect of farmers education in years on vrsTE
||Effect of farmers experience in years on crsTE
||Effect of farmers experience in years on vrsTE
||Efficiency summary in CRS and VRS assumption
|crsTE: Technical efficiency from CRS DEA; VrsTE: Technical
efficiency from VRS DEA; Scale: Scale efficiency: crsTE/vrste; *irs: Increasing
return to scale, drs: Decreasing return to scale; -: Constant return to
The empirical results indicate that there are significant possibilities to increase efficiency levels in farms.
Rakipova and Gillespie (2000) examined technical efficiency
of beef cattle producers from across Louisiana and were surveyed sixty-two producers
in fall 1998. In current research were used 70 producers.
Several farm-specific factors are analysed to assess their influence on technical
efficiency. The farmers age is defined in terms of years, while the farmers
experience and education of farmers are also defined in terms of years
and years of schooling, respectively. In addition, the number of cattle per
farm is intended to examine the impact of farm size on the technical efficiency
of the feedlot Caspian cattle farms in Iran.
In this research, farm size have influenced the crsTE of cattle farms (p<0.05).
The differences in producers age, education and experience had not different
impacts on crsTE and vrsTE of Caspian feedlot cattle farms (p>0.05) and were
same as resulted by Krasachat (2007) in Thailand.
Producers experience had not impact on TE , because farmers with more experience, had more age and in 83.1% of farms, daily work of farm were done by themselves.
Efficiency analysis of feedlot cattle farms for the case of CRS is used for
long time aims and results for the case of VRS can be used for short time aims
Beasley (2003), showed that DEA can be viewed as maximizing
the average efficiency of the Decision-Making Units (DMUs) in an organization.
The producers who have used less ME and CP achieved higher levels of allocative
and economic efficiencies and a smaller farm is likely to be economically more
efficient compared to a larger one.
As we resulted, Caspian cattle were intaked ME and CP more than their requirements. So, daily ME and CP intake of feedlot cattle must be decrease. It is required to study more about Caspian cattle requirement and factors affecting on farmers that do this study. Farmers (95%) had not mill, mixer and were used to ready concentrate. Certainly, if farmers were made concentrate themselves, had more economic efficiency.
Also, farmers (90%) were not weigh cattle during fattening period. So, they couldnt estimate ME and CP requirements with precision measurement.
All of these factors were made intaking of ME and CP inefficiently in Caspian feedlot farms. In addition, the producers who have used more labour (number days-1), achieved lower levels of allocative and economic efficiencies.
Therefore, results of this research should be used to increase the efficiencies of inefficient Caspian feedlot farms in Iran.
We can improve efficiency of Caspian cattle feedlot farms by correct using
of production sources (inputs), such as increasing length of fattening period,
decreasing number of calve per farm and decreasing total metabolizable energy
and crude protein intake of calve as recommended in Table 1.
Also, these results show that must be working on energy and protein requirements
of Caspian feedlot cattle.